From Polymer Sequence Control to Protein Recognition: Synthesis

Jul 10, 2014 - Glycopolymers with similar DIMAG fractions and molecular weights ... on the sugar rings provided further evidence that the protecting g...
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From Polymer Sequence Control to Protein Recognition: Synthesis, Self-Assembly and Lectin Binding Jiawei Lu,†,∥ Changkui Fu,‡,∥ Shiqi Wang,‡ Lei Tao,*,‡ Litang Yan,*,⊥ David M. Haddleton,§ Gaojian Chen,*,† and Yen Wei‡ †

Center for Soft Condensed Matter Physics and Interdisciplinary Research, Soochow University, Suzhou 215006, P. R. China The Key Laboratory of Bioorganic Phophorus Chemistry & Chemical Biology (Ministry of Education), Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China ⊥ Key Laboratory of Advanced Materials (MOE), Department of Chemical Engineering, Tsinghua University, Beijing 100084, P. R. China § Department of Chemistry, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, U.K. ‡

S Supporting Information *

ABSTRACT: A novel, highly efficient methodology to synthesize gradient glycopolymers has been successfully developed involving concurrent enzymatic monomer transformation and reversible addition−fragmentation chain transfer (RAFT) polymerization. By synchronizing enzymatic monomer transformation with polymerization, a continuous supply of the second monomer (glycomonomer) is achieved during the polymerization, resulting in a gradient sugar distribution in the final polymer. Detailed studies of the process using GPC and NMR indicate that the gradient glycopolymers synthesized by RAFT were well controlled. Subsequently, 1,2:3,4-di-O-isopropylidene-6-O-methacryloyl-α-D-galactopyranose (DIMAG) moieties were deprotected to regenerate the sugar and achieve amphiphilic bioactive glycopolymers. We demonstrate the synthesis of a set of glycopolymers with different sequential structures, such as statistical, gradient and block glycopolymers. The glycopolymers with block structure show higher affinities toward the RCA120 lectin receptor compared with other structural counterparts. Furthermore, simulation of the self-assembly of three types of copolymers and their binding to lectins provides fundamental insight into this result, revealing the mechanisms underlying the dependence of self-assembling structures and protein adsorption kinetics on the molecular architectures of copolymers.

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INTRODUCTION Molecular recognition, observed in DNA−protein, sugar−lectin and protein−oligopeptide interactions, is essential for life. The arrangement and sequence of recognizing moieties are important in every corner of the living body and play a vital role in numerous biological actions. For example, the arrangement of the base pairs in DNA contains specific and essential information.1 Sequence-selective recognition of peptides is a necessary process for the site-specific recognition of protein surfaces,2 which leads to the control of protein function and the understanding of biological events.3,4 As mimics of natural molecules, synthetic materials with welldefined structures are important for investigating recognition processes. Compared with DNA and peptides, the synthesis and purification of well-defined saccharides is difficult. Since the advent of controlled radical polymerization (CRP),5−13 glycopolymers have been synthesized and studied for scientific interest and technological application. Glycopolymers,14−18 which are able to interact with lectins as multivalent ligands in a manner similar to natural glycoproteins, could potentially be © 2014 American Chemical Society

used to combat infections as drug delivery systems and for other biological sensing devices due to the biofunctionality of sugars on the particle surface.19 The architecture and sequence of glycopolymers also play critical roles in lectin binding and life phenomena. Glycopolymers with different architectures can show different lectin-binding capacities.17 The self-assembly morphology of glycopolymers is also important for lectin binding and cell recognition.20 The number, density and even the neighboring effects of glycopolymers have all been found to contribute to specific carbohydrate-lectin interactions.21 Therefore, various controlled living polymerization techniques have been employed for the synthesis of functional glycopolymers with desired chain lengths, structures, and compositions. Among the different polymer structures, gradient copolymers, a novel class of polymers possessing a gradient composition from predominantly one monomer species to a Received: April 2, 2014 Revised: June 26, 2014 Published: July 10, 2014 4676

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self-assemble in water and interact with lectin (RCA120) are discussed. Synthesis of Glycopolymers. A one-pot synthetic strategy to prepare gradient copolymers proceeded through concurrent RAFT polymerization and in situ enzymatic monomer transformation.25 Typically, 1,2:3,4-di-O-isopropylidene-α-D-galactopyranose, CTA, ABVN, TFEMA, Novozym 435, and triethylamine (TEA) were added to the reaction system, using toluene as the solvent. The reaction was carried out at 50 °C, and 1H NMR was used to monitor the reaction. TFEMA was gradually converted to 1,2:3,4-di-O-isopropylidene-6-O-methacryloyl-αD-galactopyranose (DIMAG) via enzymatic monomer transformation as the reaction progressed, Figure 1; meanwhile,

second monomer species along the copolymer backbone, have shown many unique properties.22 However, the synthesis and application of gradient glycopolymers has seldom been reported. To date, only Charreyre et al.23 and Stenzel et al.24 have reported the synthesis of gradient glycopolymer architectures by the RAFT process, in which the copolymerization of two monomers produces glycopolymers with a very slight gradient microstructure; while the operation of semibatch copolymerization remains complicated. Enzymes can be regarded as green catalysts for a variety of reactions. Compared with conventional synthetic catalysts, enzymes appear to be more environmentally friendly and can often be used under milder conditions with high efficiency and high selectivity.25 Recently, we have developed a novel one-pot method to prepare gradient copolymers by synchronizing enzymatic monomer transformation with RAFT polymerization; the gradient sequence along the polymer chain could be easily tuned by changing the enzyme amount.26 A one-pot strategy is desired by synthetic chemists as it avoids an arduous separation and purification of intermediates, which improves chemical yields and saves time and resources.27−38 Herein, we demonstrate a novel one-pot polymerization strategy (Scheme 1) to prepare amphiphilic gradient Scheme 1. One-Pot Synthesis of the Gradient Glycopolymer via Concurrent Enzymatic Monomer Transformation and RAFT Polymerization

Figure 1. 1H NMR spectra (CDCl3) of the reaction at different times. [CETPA] = 0.057 mmol; [ABVN] = 0.02 mmol; [TFEMA]0 = 6.0 mmol; [DIG]0 = 6.0 mmol; [TEA] = 6.0 mmol; [Novozym 435] = 62.5 mg mL−1 in 8.0 mL of toluene at 50 °C.

both TFEMA and the newly produced DIMAG were polymerized via RAFT polymerization. The proportion of DIMAG in the polymer increased gradually from 0%, suggesting that a gradient monomer sequence was obtained. The total conversion of the monomer to the glycopolymer reached 65% after 11 h, and almost no TFEMA remained in the system. Thus, the polymerization process could be interpreted as the homopolymerization of DIMAG, which proceeded to 87% conversion after 24 h. During the course of the reaction, polymers with controlled molecular weights and narrow dispersity (PDIs ∼ 1.30) were obtained (Figure 2a and 2b), confirming the controllable features of this one-pot system. Our results confirmed that the glycomonomer and enzyme were

glycopolymers with desired molecular weights, compositions, and sequences by combining lipase−catalytic monomer transformation with RAFT polymerization. Compared with the conventional batch39,40 and semibatch24 techniques, this novel one-pot method has proven to be more convenient and versatile for preparing gradient glycopolymers. We also studied the self-assembly and lectin-binding capacities (RCA120) of glycopolymers with different sequence architectures, such as block, gradient and statisitical. Moreover, a theoretical simulation was used to better explain the experimental results.

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RESULTS AND DISCUSSION In this study, amphiphilic glycopolymers with different structures were synthesized as follows: (1) a gradient glycopolymer was synthesized using a one-pot strategy via concurrent enzymatic monomer transformation and living radical polymerization; (2) a block glycopolymer was obtained through the homopolymerization of DIMAG and chain extension of the PDIMAG macro-RAFT agent with 2,2,2trifluoroethyl methacrylate (TFEMA); (3) a statistical glycopolymer was synthesized by RAFT polymerization of DIMAG and TFEMA. Subsequently, a deprotection procedure was conducted to cleave off the isopropylidene groups. The abilities of the amphipathic glycopolymers with different structures to

Figure 2. (a) Kinetic plots of RAFT polymerization in the presence of in situ enzymatic monomer transformation. (b) Molecular weights and PDIs vs monomer conversion. 4677

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compatible with RAFT polymerization and that the controllable characteristics were maintained. It should be noted that the enzymatic transesterification only occurred on TFEMA monomer rather than TFEMA units in the polymer.41 This is attributed to the polymer’s steric hindrance, which leads to the polymer being unable to access the active pocket of the enzyme, as reported by Heise et al.42 The 1H NMR spectrum of the typical obtained glycopolymer (Mn,GPC = 25200, PDI = 1.29), Figure 3. The fraction of

Figure 4. Cumulative DIMAG mole fraction (FDIMAG) as a function of apparent normalized chain length in the gradient and statistical glycopolymer.

composition.43 Moreover, statistical copolymerization of TFEMA and DIMAG with an initial feeding ratio of 1:1 was performed to confirm that the gradient composition in the polymer resulted from the supply of monomer transformation rather than the differences in monomer reactivity. The FDIMAG along the apparent normalized chain length was found to be constant (∼50%), indicating that a statistical rather than a gradient copolymer of PDIMAG-co-PTFEMA was formed. Different concentrations of enzyme were also used to prepare different gradient glycopolymers (Figure S7, Supporting Information); similar results were obtained in our previous work.26 As mentioned above, the monomer transformation rate largely depends on the concentration of the enzyme, which will further affect the significance of the gradient composition. In order to investigate the effect of monomer sequence on self-assembly and lectin binding, block and statisitcal glycopolymers were also synthesized. For block glycopolymers, homopolymerization of DIMAG using CETPA and ABVN at 50 °C was first performed (Figure S3). The first-order plot was approximately linear, and the obtained polymers have controlled molecular weights and narrow dispersity (PDI = 1.1−1.2). The synthesized homopolymer (Mn,NMR = 15 700 g mol−1 and Mw/Mn = 1.10) was chosen for use in the subsequent chain extension. The chain extension of the PDIMAG macro-RAFT agent with TFEMA was monitored by GPC and 1H NMR; the GPC traces of the chain extension of this macro RAFT-CTA did not shown any shoulder, indicating excellent reinitiation compared with chain extension of a PDIMAG first block (Figure S4). The statistical copolymer was synthesized by RAFT polymerization of DIMAG and TFEMA using CETPA and ABVN in bulk at 50 °C; it was also well controlled with a linear dependence of the molecular weight with conversion (Figure S5). The GPC traces of the samples shifted toward higher molecular mass with increasing conversion (see Figure S5 in the Supporting Information).

Figure 3. 1H NMR spectrum (CDCl3) of the obtained glycopolymer.

DIMAG in the glycopolymer was calculated to be approximately 52% using the integral ratio of the peaks at 5.5 ppm (peak a) and 3.95−4.45 ppm (peaks b−g). The molecular weight (Mn,NMR) of the final obtained glycopolymer was approximately 21100 g mol−1 as determined by the integral ratio of the peaks at 5.5 ppm (peak a) and 3.25 ppm (peak l). Mn,NMR = [I(5.45−5.55) × 2/I(3.2−3.3)] × M(DIMAG) + {[(I(3.8−4.5) − 5 × I(5.45−5.55))]/I(3.2−3.3)} × M(TFEMA) + M(CETPA), where I(5.45−5.55) and I(3.2−3.3) represent the integration of H-1 of galactose and 2 × H of the “−SCH2−”, M(DIMAG), M(TFEMA), and M(CETPA) are the molecular weights of DIMAG, TFEMA, and CTA agent (CETPA). The fraction of DIMAG and the molecular weight (Mn, NMR) of glycopolymers with other structures were calculated in the same way, and the data are listed in Table 1. The cumulative mole fraction of DIMAG in the resulting gradient copolymers increased along the polymer chain, which corresponds with the supply of DIMAG by in situ enzymatic monomer transformation during the one-pot polymerization. There is evidence of the gradient structure of the copolymer according to the evolution of cumulative FDIMAG with increasing apparent normalized chain length, Figure 4. The apparent normalized chain length was the ratio of the Mn,GPC value of the polymer withdrawn periodically from the reaction to the Mn,GPC value of the final gradient copolymer at the end of the polymerization. FDIMAG reached ∼52% at the end of polymerization, which is higher than the value obtained through a conventional batch process, which yields a low gradient

Table 1. Data for Glycopolymers with Different Structures Used in the Study polymer

time (h)

convn (%)

DIMAG/TFEMA (NMR)

Mn,NMR (g mol−1)

Mn,GPCa (g mol−1)

PDIa

PDIMAG-g-PTFEMA PDIMAG-b-PTFEMAb PDIMAG-s-PTFEMA

24 24 22

87 74 82

52/48 56/44 51/49

21 100 20 500 27 300

25 200 25 700 30 000

1.29 1.12 1.18

GPC analyses of the listed polymers were performed using DMF as the eluent. bPDIMAG macro-RAFT agent (Mn,NMR = 15 700 g mol−1, Mn,GPC = 19 100 g mol−1, PDI = 1.10). a

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Glycopolymers with similar DIMAG fractions and molecular weights (Table 1) were used for the self-assembly and lectinbinding studies. To recover the amphiphilicity of the glycopolymers, a deprotection procedure was conducted to cleave the isopropylidene groups. Trifluoroacetic acid (TFA) was used in the deprotection reaction. Figure 5 shows the 1H NMR

Figure 6. DLS number distributions of the synthesized diblock glycopolymers in aqueous solution at a polymer concentration of 0.4 mg mL−1.

and statistical copolymer aggregates compared to the block copolymer aggregate can be attributed to the smaller portion of the polymer chains that consist of pure MAG; the other chains consist of a mixture of MAG and TFEMA.44,45 The mixed portions of the chains were found in the interior of the micelles, and the small amount of pure MAG was not sufficient to maintain the stability of the micelles. Therefore, some of the primary micelles aggregated to form a stable structure. The critical micellar concentration (CMC) is an important parameter for characterizing the stability of polymeric micelles. The CMC value was detected by a fluorescence (FL) technique using pyrene as a probe46 (Table 2, Figure S9). Gradient and

Figure 5. 1H NMR spectra of PDIMAG-g-PTFEMA before and after deprotection (in DMSO-d6).

spectra of PDIMAG-g-PTFEMA before and after deprotection. The disappearance of the isopropylidene signals at approximately 1.5 ppm and the slight downfield shift of the protons on the sugar rings provided further evidence that the protecting groups were quantitatively removed. Indeed, if the residual protection functions were still present, a signal at 5.55 ppm should be observed. FT-IR qualitatively showed that the broad hydroxyl band at 3400 cm−1 appeared after the deprotection and that the carbon fluorine bond at 1350−1120 cm−1 was still present, indicating that the deprotection of the sugar molecules was successful and that the ester bond linking the polymer backbone and the side-chain molecules was not affected (Figure S8). Self-Assembly of Amphiphilic Glycopolymer in Water. Understanding the aggregation behaviors of the synthesized polymers in aqueous media is an important first step for investigating their further bioapplications. The deprotected gradient, block and statistical copolymers have amphiphilic structures; therefore, micelle formation is expected in aqueous solution. In order to study and compare the aggregation properties of these copolymers, micellization of the copolymers was performed by heating the polymers in a water solution to 70 °C, followed by subsequent cooling to 20 °C. After two such heating−cooling cycles facilitating the formation of hydrodynamically stable aggregates,44 the micellar solutions were analyzed by DLS and TEM. In DLS, size by intensity were initially used and we found very big particles formed for gradient and statistical copolymers. Some of the primary small micelles may aggregate due to the presence of some hydrophobic groups on the surface, leading to bigger particles. Please note that in DLS, intensity of bigger particles is much larger than small particles, therefore the average size determined by intensity will be much larger than actuall size in the multimodal distribution systems, even the proportion of large particles is very small. Size by number were then used for comparison. The sizes of the aggregates of PMAG-g-PTFEMA and PMAG-s-PTFEMA are larger than that of PMAG-bPTFEMA, Figure 6. The relatively larger sizes of the gradient

Table 2. Self-Assembly Data for the Glycopolymers in Water diameter (nm) polymer

DLSa

TEMb

CMCc (mg mL−1)

PDIMAG-g-PTFEMA PDIMAG-b-PTFEMA PDIMAG-s-PTFEMA

48.6 21.4 83.6

36.7 22.6 41

0.021 0.155 0.026

a The copolymer concentration was 0.4 mg mL−1. bNegative staining was performed by casting a tiny drop of (2% w/w) of phosphotungstic acid (PTA) onto the already-deposited micelles. cMeasured in water at 25 °C.

statisitical copolymers presented relatively low CMC values, which are desirable when delivering drugs in vivo. TEM was also used to characterize the morphologies of the aggregates compared with statisitical polymer, Figure 7; the self-assembly morphologies of the block and gradient polymers appeared more regular than that of the statistical polymer, and the size of the aggregates of the block copolymer was smaller than those of the other copolymers. (Table 2) The differences in the sizes between the different methods were attributed to the fact that DLS measures the hydrodynamic diameter of the micelles in an aqueous solution, while TEM measures the diameter of dried micelles. Similar conclusions have been drawn in the literature.38 For the gradient and statistical copolymers, the separation of the primary micelles during the drying process when preparing the TEM samples may also explain the smaller sizes measured by TEM. Theoretical simulations of the self-assembly and lectin binding of the three types of copolymers were performed to better explain the experimental results. Figure 8 shows the equilibrium morphologies of the three types of copolymers. 4679

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(Figure 8c). No hairs can be seen on these micelles due to the statistical distribution of the hydrophobic beads. The simulation results agree with the results of theoretical analyses in which the “ball-of-yarn” and “tadpole” conformations and a disordered globular state are theoretically obtained for the block, gradient, and statistical copolymers, respectively.47 However, the particlebased simulations provide the molecular details for these structures and detailed structural formation dynamics. Lectin-Binding Properties of the Glycopolymers. To measure the biofunctionality of the glycopolymers and determine how the polymeric ligands might react in a biological system, the ligand-lectin binding abilities of the glycopolymer micelles were determined by specific binding using Ricinus communis (castor bean) agglutinin RCA120, a lectin that specifically binds to galactosyl residues. The bioactivity of the glycopolymer micelles was tested using turbidimetric assays via UV−vis spectroscopy. PMAG-g-PTFEMA, PMAG-b-PTFEMA, and PMAG-s-PTFEMA micelles were tested for their ability to form clusters with RCA120. The identity of the binding elements, the structure of the scaffold, the number of binding groups, and the density of the binding elements are all parameters that influence the mechanisms of ligand binding.48 The PMAG-g-PTFEMA micelles displayed a much faster clustering rate than the PMAG-b-PTFEMA micelles (Figure 8). The density of the multivalent galactose ligand was the same for both types of micelles, and the concentrations of both ligand solutions were kept constant. The lower clustering rate displayed by the PMAG-b-PTFEMA micelles could be caused by higher chain dynamics. In contrast, the contracted structure of the PMAG-g-PTFEMA micelles causes a tighter structure of the glycopolymers chains on the surface. It has previously been reported that rigid scaffolds of sugars are entropically favorable to enhance binding.49 Conversely, the lower clustering rate displayed by the PMAG-s-PTFEMA micelles might be a result of the micelle structure, in which two monomers in the polymer chain were arranged in the shape of an intersection, suggesting that the galactose corona was very flimsy. However, the binding abilities of the PMAG-g-PTFEMA and PMAG-b-PTFEMA micelles were greatly enhanced due to the stronger multivalent effects caused by a sufficient numbers of galactose groups on the surface of the micelles.11 The aggregates formed by the PMAG-g-PTFEMA micelles and RCA120 were later treated with an excess of D-galactose, a competitive ligand. The competitive binding assay led to a decrease in the absorbance, as the stronger ligand for RCA120 reverses the binding of the galactose in the micelles containing RCA120 (Figure 9).49 As turbidity assay relies on the formation of particles that are big enough to scatter light, the ability to form large particles may not be identical to the ability of lectin-binding. To get more information about the lectin-binding of the three glycopolymers, quartz crystal microbalance-dissipation (QCMD) were further used for evaluation. Figure 10a showed that the frequency shift (Δf) of the PMAG-b-PTFEMA coated QCM chip decreased when RCA120 buffer solution passed through the glycopolymer surface, giving a clear dose-dependent response. The binding constant Ka of the RCA120 and PMAG-bPTFEMA was evaluated by the Langmuir adsorption model. The plot according to the Langmuir adsorption equation generated a straight line and the ratio of slope to intercept is Ka, as shown in Figure 10b. The concentration range of RCA120 was from 5 to 320 μg mL−1. The association constant value determined by QCM is 2.165 × 106 mol L−1. Figure 10c and

Figure 7. Transmission electron microscope images of the micelles with different architectures: (a) PMAG-b-PTFEMA; (b) PMAG-gPTFEMA; (c) PMAG-s-PTFEMA. Scale bars are 50 nm for part a and 100 nm for parts b and c.

Figure 8. Equilibrium morphologies formed by the self-assembly of various copolymers in water. (a) block copolymers, (b) gradient copolymers, and (c) statistical copolymers. The representative structures of micelles in these three systems are displayed at the bottom of each snapshot. The green and red tubes represent the hydrophilic and hydrophobic sections of each copolymer chain. The solvent beads are not shown for clarity.

The detailed structural evolution dynamics corresponding to these structures are shown in Figures S11−S13. The copolymers in all these systems self-assemble into micelle structures, but their morphologies depend significantly on the molecular architectures of the copolymers. The bottom snapshots display enlarged representative micelles for each system. For micelles of the block copolymers, the hydrophobic blocks collapse into a core, with the hydrophilic blocks extending into the solvent (Figure 8a). Figure 8b shows that the gradient copolymers also self-assemble into a micelle with a core and hydrophilic hairs. However, in contrast to the block copolymer micelles, the length of the hydrophilic hairs is shorter and the micelle core is looser because the gradient and alternating distribution of the hydrophilic beads hinders the formation of a packed core. This inhibition effect becomes more obvious in micelles formed by the statistical copolymer, in which almost no collapsed hydrophobic core can be identified 4680

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hydrophilic blocks of the copolymers. Figure 11a−c presents the equilibrium morphologies for these systems. The extending

Figure 9. Turbidimetry assays of amphiphilic glycopolymer micelles of different structures with RCA120 association binding, and dissociation competitive assays of the micelles with RCA120 after the addition of D(+)-galactose.

Figure 11. Morphologies formed from the self-assembly of proteins on various equilibrium copolymers: (a) block copolymers, (b) gradient copolymers, and (c) statisitical copolymers. (d) Binding ratio of proteins on copolymers, f b, vs time for three types of copolymers. The solvent beads are not shown for clarity.

hydrophilic hairs of the micelles composed of the block and gradient copolymers significantly facilitate the binding of protein particles. However, the absence of hairs on the micelle composed of statistical copolymers limits the binding of proteins, and thus, more discrete protein particles can be found in this system (Figure 11c). To quantify the degree of protein binding to the polymers, the parameter f b, defined as the ratio between the number of bound proteins and the total number of proteins, was monitored during the self-assembly of these copolymers. Figure 11d shows the plots of the temporal evolution of f b. Indeed, the proteins combined with block copolymers display the fastest binding kinetics and the highest binding efficiency, but both the protein-binding kinetics and binding efficiency were the lowest for the statisitical copolymer. Our simulation results highlight the importance of the hairy structure of a micelle for protein binding, which can ultimately be attributed to the unique molecular architectures of the block and gradient copolymers. It is worth noting that the simulation results appear to be in agreement with QCM results, while slightly different from the turbidimetry assays, for which the absorption kinetics of the gradient copolymers was found to be the fastest. However, this difference does not contradict our conclusions and can be ascribed to the different characterization approaches used to determine the binding degree. For the simulations, a protein is identified as binding with a copolymer when one of its binding sites is within a certain distance (0.7rc) of a block copolymer. However, the absorption in the turbidimetry assays directly relates to the rate of receptor-receptor association by the polymer.50 Although more protein particles can bind to the block copolymers, the longer hydrophilic hairs of these micelles significantly reduce the concentration of the protein particles and lead to a higher degree of particle dispersion than seen with

Figure 10. QCM-D plots. (a) Frequency shift of PMAG-b-PTFEMA functionalized gold-coated chip vs time for the adsorption of Con A with various concentration; (b) Langmuir isotherm plot of C/Δf against C; (c) binding constant Ka of the RCA120 and glycopolymer with different structures.

Figures S14 and S15 present the Ka of the RCA120 and glycopolymers with different structures. The results show that block glycopolymer had the preferably binding ability to the lectin, then gradient glycopolymer, and finally stastistical glycopolymer. We found different results of lecin-binding from turbidity assay and QCM. Computer simulations were carried out to understand the lectin-binding capabilities of the three different glycopolymers. One hundred protein particles (volume fraction, 0.05) were randomly added into systems in which each protein initially maintains a sufficiently large distance from the polymers. The proteins are then directed to attach to the 4681

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gradient copolymers, as shown in Figure 11a,b. Thus, the complex of gradient copolymers has a higher tendency to segregate from the solvent, as demonstrated by the turbidimetry assays. In QCM, the binding constant Ka obtained is fundamentally more close to the binding in simulations, therefore the outcome of lecin-binding abilities from QCM are in good agreement with simulation results. Above all, the simulations provide a fundamental understanding of the mechanisms underlying the self-assembling structures and the protein adsorption kinetics on the molecular architectures of copolymers.

CONCLUSION In conclusion, we have developed a novel one-pot method to prepare gradient glycopolymers by combining enzymatic monomer transformation with RAFT polymerization. In this method, the highly efficient enzymatic transesterification provided an increasing supply of the second monomer over the course of the polymerization, resulting in a gradient monomer sequence distribution along the polymer chain. Through this one-pot integrated synthetic method, various gradient glycopolymers could be obtained by changing the concentration of the enzyme. This method is simple, highly efficient and provides an alternative strategy for monomer sequence control in polymer chemistry. We investigated the influence of the macromolecule architecture, i.e., block, statistical, and gradient copolymers, on the self-assembly and binding behavior toward RCA120. The block and gradient structures exhibited superior lectin-binding capability than statistical via experimental procedures, and both the selfassembly and binding mechanisms were further studied using simulations. The particle-based simulations provide molecular details and detailed structural formation dynamics for these structures. Simulation results highlight the important influence of the hairy structure of micelles on protein binding, which can be attributed to the unique molecular architectures of the block and gradient copolymers. ASSOCIATED CONTENT

S Supporting Information *

Experimental section, materials, and instrumentation as well as more detailed experimental data and spectra. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected] (G.C.). *E-mail: [email protected] (L.T.). *E-mail:[email protected]. (L.Y.). Author Contributions ∥

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These authors contributed equally.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (21374069, 20104039, 21174080 and 51273105) and the Scientific Research Foundation for Returned Overseas Chinese Scholars, State Education Ministry. The authors are very thankful for the help of Yafei Luan and Professor Hong Chen for QCM-D experiments. 4682

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